GENERATIVE AI-DRIVEN PROJECT MANAGEMENT: INTELLIGENT PLANNING, PREDICTIVE RESOURCE ALLOCATION, AND CONTEXTAWARE STAKEHOLDER ENGAGEMENT

Abstract

The integration of Generative Artificial Intelligence (GenAI) into project management is reshaping conventional management practices by enabling intelligent decision support, adaptive planning, and human–AI collaboration. This review examines the application of GenAI across project planning, resource allocation, scheduling, risk management, and stakeholder communication, synthesizing academic research, empirical evidence, and emerging enterprise platform architectures. It explores how transformer-based large language models (LLMs) can complement established project management methodologies by transforming static planning processes into dynamic, iterative workflows that respond to changing project requirements, resource constraints, and stakeholder expectations. Particular attention is given to hybrid architectures that integrate LLMs with retrieval-augmented generation (RAG), knowledge graphs, and deterministic scheduling techniques, including the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT). These combinations offer a promising foundation for improving planning accuracy, resource utilization, schedule reliability, and managerial decision-making, although reported performance gains require careful interpretation in light of differences in study design, implementation context, and evaluation methods. The review also investigates multi-agent AI systems that coordinate specialized agents to automate administrative tasks, support cross-functional collaboration, and facilitate context-aware stakeholder engagement. Despite these opportunities, successful implementation remains constrained by hallucinations, data privacy risks, algorithmic bias, inadequate governance, overreliance on automated recommendations, and potential erosion of professional expertise. To address these challenges, this study proposes a socio-technical framework integrating human-in-the-loop oversight, transparent decision-making, secure data management, systematic AI evaluation, and continuous workforce development. The review establishes a research-oriented foundation for responsible GenAI adoption in project management and identifies priorities for future empirical validation, organizational readiness assessment, and the development of trustworthy human–AI project management ecosystems.